标签相关变压器用于自动胸部X射线诊断,具有可靠的可解释性
Zexuan Sun1,2, Linhao Qu1,2, Jiazheng Luo1,2
1Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai, 200032, China.
La Radiologia medica
|May 26, 2023
概括
这项研究引入了一种基于变压器的新型深度学习模型,用于改善胸部X射线 (CXR) 图像的计算机辅助诊断 (CAD),提高疾病查的准确性和可解释性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是指放射学
背景情况:
- 胸部X射线 (CXR) 图像的计算机辅助诊断 (CAD) 通过减少疾病查工作负载和观察者之间的变化来帮助放射科医生.
- 目前用于多标签CXR分类的深度学习方法在准确性和可解释性方面面临挑战.
研究的目的:
- 为自动化CXR诊断提出一种基于变压器的新型深度学习模型.
- 提高分类性能,为诊断任务提供可靠的解释性.
主要方法:
- 开发了一种新的变压器架构,以捕获全球和本地图像信息和标签相关性.
- 引入了一个新的损失函数,用于识别CXR图像中的标签之间的相关性.
- 为模型解释性生成了注意热图,将模型焦点与医生标记的病原体区域进行比较.
主要成果:
- 拟议的模型在胸部X射线14数据集上实现了0.831的平均AUC,在PadChest数据集上达到0.875.
- 在CXR多标签分类中,性能超过了现有的最先进的方法.
- 注意热图表明该模型能够专注于相关的病原性区域.
结论:
- 基于变压器的模型显著提高了自动CXR诊断的性能和可解释性.
- 这些发现为自动疾病查的临床应用提供了新的证据和方法.
- 该模型通过将注意力热图与真正的病原体区域相关联,提供可靠的解释性.
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